Gaussian Process-Based Real-Time Learning for Safety Critical Applications

Armin Lederer, Alejandro J Ordóñez Conejo, Korbinian A Maier, Wenxin Xiao, Jonas Umlauft, Sandra Hirche
Proceedings of the 38th International Conference on Machine Learning, PMLR 139:6055-6064, 2021.

Abstract

The safe operation of physical systems typically relies on high-quality models. Since a continuous stream of data is generated during run-time, such models are often obtained through the application of Gaussian process regression because it provides guarantees on the prediction error. Due to its high computational complexity, Gaussian process regression must be used offline on batches of data, which prevents applications, where a fast adaptation through online learning is necessary to ensure safety. In order to overcome this issue, we propose the LoG-GP. It achieves a logarithmic update and prediction complexity in the number of training points through the aggregation of locally active Gaussian process models. Under weak assumptions on the aggregation scheme, it inherits safety guarantees from exact Gaussian process regression. These theoretical advantages are exemplarily exploited in the design of a safe and data-efficient, online-learning control policy. The efficiency and performance of the proposed real-time learning approach is demonstrated in a comparison to state-of-the-art methods.

Cite this Paper

BibTeX
@InProceedings{pmlr-v139-lederer21a, title = {Gaussian Process-Based Real-Time Learning for Safety Critical Applications}, author = {Lederer, Armin and Conejo, Alejandro J Ord{\'o}{\~n}ez and Maier, Korbinian A and Xiao, Wenxin and Umlauft, Jonas and Hirche, Sandra}, booktitle = {Proceedings of the 38th International Conference on Machine Learning}, pages = {6055--6064}, year = {2021}, editor = {Meila, Marina and Zhang, Tong}, volume = {139}, series = {Proceedings of Machine Learning Research}, month = {18--24 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v139/lederer21a/lederer21a.pdf}, url = {https://proceedings.mlr.press/v139/lederer21a.html}, abstract = {The safe operation of physical systems typically relies on high-quality models. Since a continuous stream of data is generated during run-time, such models are often obtained through the application of Gaussian process regression because it provides guarantees on the prediction error. Due to its high computational complexity, Gaussian process regression must be used offline on batches of data, which prevents applications, where a fast adaptation through online learning is necessary to ensure safety. In order to overcome this issue, we propose the LoG-GP. It achieves a logarithmic update and prediction complexity in the number of training points through the aggregation of locally active Gaussian process models. Under weak assumptions on the aggregation scheme, it inherits safety guarantees from exact Gaussian process regression. These theoretical advantages are exemplarily exploited in the design of a safe and data-efficient, online-learning control policy. The efficiency and performance of the proposed real-time learning approach is demonstrated in a comparison to state-of-the-art methods.} }
Endnote
%0 Conference Paper %T Gaussian Process-Based Real-Time Learning for Safety Critical Applications %A Armin Lederer %A Alejandro J Ordóñez Conejo %A Korbinian A Maier %A Wenxin Xiao %A Jonas Umlauft %A Sandra Hirche %B Proceedings of the 38th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2021 %E Marina Meila %E Tong Zhang %F pmlr-v139-lederer21a %I PMLR %P 6055--6064 %U https://proceedings.mlr.press/v139/lederer21a.html %V 139 %X The safe operation of physical systems typically relies on high-quality models. Since a continuous stream of data is generated during run-time, such models are often obtained through the application of Gaussian process regression because it provides guarantees on the prediction error. Due to its high computational complexity, Gaussian process regression must be used offline on batches of data, which prevents applications, where a fast adaptation through online learning is necessary to ensure safety. In order to overcome this issue, we propose the LoG-GP. It achieves a logarithmic update and prediction complexity in the number of training points through the aggregation of locally active Gaussian process models. Under weak assumptions on the aggregation scheme, it inherits safety guarantees from exact Gaussian process regression. These theoretical advantages are exemplarily exploited in the design of a safe and data-efficient, online-learning control policy. The efficiency and performance of the proposed real-time learning approach is demonstrated in a comparison to state-of-the-art methods.
APA
Lederer, A., Conejo, A.J.O., Maier, K.A., Xiao, W., Umlauft, J. & Hirche, S.. (2021). Gaussian Process-Based Real-Time Learning for Safety Critical Applications. Proceedings of the 38th International Conference on Machine Learning, in Proceedings of Machine Learning Research 139:6055-6064 Available from https://proceedings.mlr.press/v139/lederer21a.html.

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